Helicopter detection and classification using hidden Markov models

Hidden Markov models (HMMs) are probabilistic finite state machines that can be used to represent random discrete time data. HMMs produce data through the use of one or more "observable" random processes. An additional "hidden" Markov process controls, which of the "observable" random processes is used to produce an individual data observation. Helicopter radar signatures can be represented as quasiperiodic one-dimensional discrete time series that can be analyzed using HMMs. In the HMM helicopter detection and classification algorithm developed in this study, the states of the "hidden portion" of the HMM were used to represent time dependent alignments between the radar and helicopter rotor structures. For example, the times when specular reflections occur were used to define a "blade-flash" state. Since blade-flash frequency, and the corresponding non-blade-flash state duration, is an important feature in helicopter detection and classification, HMMs that allowed direct specification of state duration probabilities were use in this study. The HMM approach was evaluated using X-Band radar data from military helicopters recorded at Ft. A.P. Hill. After initial adaptive clutter suppression and blade-flash enhancement preprocessing, a set of approximately 1,000 raw in-phase and quadrature (I/Q) data records were analyzed using the HMM approach. A correct target classification rate that varied between 98% for a PRF of 10 KHz to 91% at a 2.5 KHz PRF was achieved.